Method for detecting and analyzing site quality

ABSTRACT

A method and system for analyzing sites is described. A scanner module scans listings of an online publication. A quality engine analyzes the quality of the listings. A data collection module collects defective conditions of the listings determined by the quality engine. A report module reports the defective conditions of the listings. An auto correction module automatically corrects at least a first portion of the defective conditions of the listings. A manual correction module enables an operator of the online publication to correct at least a second portion of the defective conditions of the listings.

CLAIM OF PRIORITY

This Application is a Continuation of U.S. application Ser. No.16/532,940, filed Aug. 6, 2020, which is a Continuation of U.S.application Ser. No. 16/139,336, filed Sep. 24, 2018, which is aContinuation of U.S. application Ser. No. 13/538,934, filed Jun. 29,2012, each of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

This application relates to a computer network system, in particular, toa method and system for detecting and analyzing website quality.

BACKGROUND

The World Wide Web available on the Internet provides a variety ofspecially formatted documents called web pages. The web pages aretraditionally formatted in a language called HTML (HyperText MarkupLanguage). Many web pages include links to other web pages which mayreside in the same website or in a different website, and allow users tojump from one page to another simply by clicking on the links. The linksuse Universal Resource Locators (URLs) to jump to other web pages. URLsare the global addresses of web pages and other resources on the WorldWide Web.

Hosted web pages include links that use URLs. However, as web technologyevolves, websites have become more and more complex and human error isprone to cause defective web pages that include links that are no longeravailable. Manual correction of the broken links and other errors on theweb pages would take an enormous amount of time.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a network system, according to oneembodiment, having a client-server architecture configured forexchanging data over a network;

FIG. 2 is a block diagram illustrating an example embodiment of a sitequality analyzer;

FIG. 3 is a block diagram illustrating an example embodiment of aquality engine;

FIG. 4 is a block diagram illustrating an example of a bad conditionsreport module;

FIG. 5 is a block diagram illustrating an example of an auto correctionmodule;

FIG. 6 is a flow chart of an example method for analyzing listing sitesin an online publication;

FIG. 7 is a flow chart of an example method for detecting defects inlisting sites in an online publication;

FIG. 8 is a flow chart of an example method for reporting defects inlisting sites in an online publication; and

FIG. 9 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions may beexecuted to cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

Although the present invention has been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the present disclosure.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

In various embodiments, a method and system for analyzing sites isdescribed. A scanner module scans listings of an online publication. Aquality engine analyzes the quality of the listings. A data collectionmodule collects defective conditions of the listings determined by thequality engine. A report module reports the defective conditions of thelistings. An auto correction module automatically corrects at least afirst portion of the defective conditions of the listings. A manualcorrection module enables an operator of the online publication tocorrect at least a second portion of the defective conditions of thelistings.

In one embodiment, a publication application generates the onlinepublication comprising listings for an electronic commerce website.

In one embodiment, the quality engine analyzes the listings fordefective links, defective images, defective formats, and policyviolations of the online publication.

In one embodiment, the report module reports the defective conditions ofthe listings to the corresponding posting users of the onlinepublication.

In one embodiment, the report module reports the defective conditions ofthe listings to an operator of the online publication.

In one embodiment, the auto correction module repairs links, images, andformats based on the defective conditions of the listings.

In one embodiment, the auto correction module deletes a listing thatviolates a policy of the online publication.

In one embodiment, a learning module of the auto correction modulelearns from the defective conditions of the listings collected by thedata collection module.

The scanner module may continuously scan the sites and analyze the sitesfrom a quality standpoint. Some of the things the scanner module detects& analyzes are broken links, images without alt text, descriptionscontaining external links, policy enforcement violations, broken images,and so forth. In addition, the site quality analyzer collects additionaldata pertinent to each of the ‘bad conditions’ in such a way that it iseasily fixable by Quality Assurance (QA) and Engineering teams of thenetwork-based publisher 102. An example is providing metadata (like textnames for hyperlinks) for the broken links. In another embodiment, thecrawl and analysis may be prioritized based on the frequency of the pageusage. For example, pages or sites that are accessed the mostfrequently, are prioritized for the crawl and analysis.

FIG. 1 is a network diagram depicting a network system 100, according toone embodiment, having a client-server architecture configured forexchanging data over a network. For example, the network system 100 maybe a network-based publisher 102 where clients may communicate andexchange data within the network system 100. The data may pertain tovarious functions (e.g., online item purchases) and aspects (e.g.,managing content and user reputation values) associated with the networksystem 100 and its users. Although illustrated herein as a client-serverarchitecture as an example, other embodiments may include other networkarchitectures, such as a peer-to-peer or distributed networkenvironment.

A data exchange platform, in an example form of a network-basedpublisher 102, may provide server-side functionality, via a network 104(e.g., the Internet) to one or more clients. The one or more clients mayinclude users that utilize the network system 100 and more specifically,the network-based publisher 102, to exchange data over the network 104.These transactions may include transmitting, receiving (communicating)and processing data to, from, and regarding content and users of thenetwork system 100. The data may include, but are not limited to,product and service listings associated with buyers and sellers; contentand user data such as feedback data; user reputation values; userprofiles; user attributes; product and service reviews; product,service, manufacturer, and vendor recommendations and identifiers;auction bids; and transaction data, among other things.

In various embodiments, the data exchanges within the network system 100may be dependent upon user-selected functions available through one ormore client or user interfaces (UIs). The UIs may be associated with aclient machine, such as a client machine 106 using a web client 110. Theweb client 110 may be in communication with the network-based publisher102 via a web server 120. The UIs may also be associated with a clientmachine 108 using a programmatic client 112, such as a clientapplication, or a third party server 114 hosting a third partyapplication 116. It can be appreciated that, in various embodiments, theclient machine 106, 108, or third party server 114 may be associatedwith a buyer, a seller, a third party electronic commerce platform, apayment service provider, or a shipping service provider, each incommunication with the network-based publisher 102 and optionally eachother. The buyers and sellers may be any one of individuals, merchants,or service providers, among other things.

Turning specifically to the network-based publisher 102, an applicationprogram interface (API) server 118 and a web server 120 are coupled to,and provide programmatic and web interfaces respectively to, one or moreapplication servers 122. The application servers 122 host one or moreapplications (a publication application 124 and a site quality analyzer130). The application servers 122 are, in turn, shown to be coupled toone or more database server(s) 126 that facilitate access to one or moredatabase(s) 128.

In one embodiment, the web server 120 and the API server 118 communicateand receive data pertaining to listings, transactions, and feedback,among other things, via various user input tools. For example, the webserver 120 may send and receive data to and from a toolbar or webpage ona browser application (e.g., web client 110) operating on a clientmachine (e.g., client machine 106). The API server 118 may send andreceive data to and from an application (e.g., programmatic client 112or third party application 116) running on another client machine (e.g.,client machine 108 or third party server 114).

The publication application 124 may provide a number of publisherfunctions and services (e.g., listing, payment, etc.) to users thataccess the network-based publisher 102. For example, the publicationapplication 124 may provide a number of services and functions to usersfor listing goods and/or services for sale, facilitating transactions,and reviewing and providing feedback about transactions and associatedusers. Additionally, the publication application 124 may track and storedata and metadata relating to financial transactions among users of thenetwork-based publisher 102. In one embodiment, the listings of productsand/or services may include links to external websites, links to mediasuch as pictures and video. The listings may include media content suchas photos, audio, and video. The listings may also include metadataassociated with the content of the listing. For example, the listing mayinclude key words associated with the content of a correspondinglisting.

A third party application 116 may execute on a third party server 114and may have programmatic access to the network-based publisher 102 viathe programmatic interface provided by the API server 118. For example,the third party application 116 may use information retrieved from thenetwork-based publisher 102 to support one or more features or functionson a website hosted by the third party. The third party website may, forexample, provide one or more listing, feedback, publisher or paymentfunctions that are supported by the relevant applications of thenetwork-based publisher 102.

The site quality analyzer 130 analyzes listings posted with thepublication application 124. In particular, the site quality analyzer130 scans the listings for defects and can automatically correct somedefects in the listings.

FIG. 2 is a block diagram illustrating an example embodiment of the sitequality analyzer 130. The site quality analyzer 130 has a scanner module202, a quality engine 204, a bad conditions data collection module 206,a bad conditions report module 208, an auto correction module 210, and amanual correction module 212.

The scanner module 202 scans listings from the online publication. Inone embodiment, a web crawler crawls through the different layers ofpages published by the publication application 124 to identify thedifferent listings. For example, a listing may include elements such asthe title of the listing, the name of an item to be sold, the askingprice of the item, a description of the item, pictures of the items,videos of the item. In another embodiment, the scanner module 202 crawlsthrough the listings for site quality (e.g., title, image, descriptionmatch).

The quality engine 204 analyzes the quality of the listings. Forexample, the quality engine 204 checks on whether the links in thewebpage or site of the listing are defective, whether links to images orother media are valid, whether links to external pages are valid,whether the title of the listing corresponds to the name of the item tobe sold in the listing, whether the image displayed in the listing sitedoes not correspond to the item listed, whether the product descriptionis inaccurate, does not match with the listed item, or has typographicalerrors. Furthermore, in another embodiment, the quality engine 204 mayanalyze the site of the listing from a search engine optimizationperspective to determine how healthy the site is, or whether the site ofthe listing complies with predefined best practices. Details of thequality engine 204 are described below with respect to FIG. 3 .

The bad conditions data collection module 206 collects defectiveconditions of the listings as determined by the quality engine 204. Thedefective conditions data include, for example, listings with defectivelinks, defective images, defective formats, and policy violations. Thedefective conditions data may be stored in a storage device for furtheranalysis and in order to provide data to a learning module.

The bad conditions report module 208 reports the defective conditions ofthe listings to the corresponding posting users and/or an operator ofthe publication application 124. For example, the bad conditions reportmodule 208 may notify a posting user of the listing that the title ofthe listing does not match or correspond to the item being listed. Forexample, a posting user may have labeled “CD player” as a title andlisted and described a DVD player in the listing. In another embodiment,the bad conditions report module 208 may notify an operator of thepublication application 124. For example, the operator may be notifiedif the listing violates a policy of the publication application 124. Forexample, the operator may be notified when a new listing includes anitem for sale that is prohibited by the policy of the publicationapplication 124. Details of the conditions report module 208 aredescribed below with respect to FIG. 4 .

The auto correction module 210 automatically corrects at least a firstportion of the defective conditions of the listings. The manualcorrection module 212 enables an operator of the online publication tocorrect at least a second portion of the defective conditions of thelistings. Details of the operation of auto correction module 210 arefurther described below with respect to FIG. 5 .

In another embodiment, the auto correction module 210 is an offlinemodule that works off the data downloaded from the defective web pagesafter the analysis is performed. A manuals correction may still berequired by a web administrator who makes decision to remove defectiveweb pages or listings (based on listing policies).

FIG. 3 is a block diagram illustrating an example embodiment of thequality engine 204 of FIG. 2 . The quality engine 204 may include adefective links module 302, a defective images module 304, a defectiveformats module 306, and a policy violations module 308.

The defective links module 302 of the quality engine 204 checks whetherlinks provided on the listing page are valid. For example, a crawler canclick on all web links provided on the listing page one level at a timeto determine their validity. In other words, the defective links module302 identifies broken links located on the listing page.

The defective image module 304 of the quality engine 204 checks forbroken images and whether the images on the listing page are valid. Thesource code of the listing page may include links to images external tothe publication application 124. The links for the images may beoutdated or defective.

The defective formats module 306 of the quality engine 204 checks fordetective formatting of the listing. For example, the listing mayinclude a defective header format or other types of defective dataformat.

The policy violations module 308 of the quality engine 204 checks forlistings that violate a policy of the publication application 124.

In other embodiments, the quality engine 204 may also check fortypographical and grammatical errors. The quality engine 204 may includeadditional modules to determine the level of quality of the site or pageof the listing published by the publication application 124.

FIG. 4 is a block diagram illustrating an example of the bad conditionsreport module 208 of FIG. 2 . The bad conditions report module 208includes a listing poster notification module 402 and a listing ownernotification module 404.

The listing poster notification module 402 notifies the poster of thelisting of the defective listings. For example, the listing posternotification module 402 notifies the posting user of the listing for aDVD player for sale that the image link is broken. In one embodiment,the listing is unpublished or pending while the listing posternotification module 402 notifies the posting user of the defect.

The listing owner notification module 404 notifies an operator of thepublication application 124 of the defective listings. The operator ofthe publication application 124 may be the owner of the publicationapplication 124. For example, the listing owner notification module 404notifies the operator that a listing violates a policy because itincludes items forbidden by the publication application 124. In oneembodiment, the listing is unpublished or pending while the listingowner notification module 404 notifies the operator of the publicationapplication 124.

FIG. 5 is a block diagram illustrating an example of the auto correctionmodule 210 of FIG. 2 . The auto correction module 210 includes, forexample, a repair links module 502, a repair images module 504, a repairformats module 506, a delete listing module 508, and a learning module510.

The repair links module 502 automatically repairs defective links asidentified by the defective links module 302 of the quality engine 204.In one embodiment, the repair links module 502 determines the correctlink using the bad conditions data collected by bad conditions datacollection module 206 and/or using the learning module 510.

The repair images module 504 automatically repairs defective or brokenimages as identified by the defective images module 304 of the qualityengine 204. In one embodiment, the repair images module 504 determinesthe correct images using the bad conditions data collected by badconditions data collection module 206 and/or using the learning module510.

The repair formats module 506 automatically repairs defective formatsfrom the listing as identified by the defective formats module 306 ofthe quality engine 204. In one embodiment, the repair formats module 506determines the correct format using the bad conditions data collected bybad conditions data collection module 206 and/or using the learningmodule 510.

The delete listing module 508 of the auto correction module 210automatically deletes listings that cannot be corrected automatically orlistings that violate a policy of the publication application 124. Inone embodiment, listings that are not corrected by the posting userwithin a predetermined amount of time may be deleted.

FIG. 6 is a flow chart 600 of an example method for analyzing listingsites in an online publication. At operation 602, the publicationapplication 124 receives listings for publication.

At operation 604, the scanner module 202 scans for listings of an onlinepublication.

At operation 606, the quality engine 204 analyzes the quality of thelistings.

At operation 608, the report module 208 reports bad conditions oflistings having defects.

At operation 610, the auto correction module 210 automatically correctsthe bad conditions in the listings identified with defects, and themanual correction module 212 enables an operator of the onlinepublication to correct at least a second portion of the defectiveconditions of the listings.

FIG. 7 is a flow chart 700 of an example method for detecting defects inlisting sites in an online publication. At operation 702, the qualityengine 204 analyzes the listings for defective links. At operation 704,the quality engine 204 analyzes the listings for defective images. Atoperation 706, the quality engine 204 analyzes the listings fordefective formats. At operation 708, the quality engine 204 analyzes thelistings for policy violations of the online publication.

FIG. 8 is a flow chart 800 of an example method for reporting defects inlisting sites in an online publication. At operation 802, the badconditions report module 208 reports defective conditions of thelistings to the corresponding posting users of the online publication.At operation 804, the bad conditions report module 208 reports defectiveconditions of the listings to an operator of the online publication.

FIG. 9 shows a diagrammatic representation of machine in the exampleform of a computer system 900 within which a set of instructions may beexecuted causing the machine to perform any one or more of themethodologies discussed herein. In alternative embodiments, the machineoperates as a standalone device or may be connected (e.g., networked) toother machines. In a networked deployment, the machine may operate inthe capacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 900 includes a processor 902 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 904 and a static memory 906, which communicate witheach other via a bus 908. The computer system 900 may further include avideo display unit 910 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 900 also includes analphanumeric input device 912 (e.g., a keyboard), a user interface (UI)navigation device 914 (e.g., a mouse), a disk drive unit 916, a signalgeneration device 918 (e.g., a speaker) and a network interface device920.

The disk drive unit 916 includes a machine-readable medium 922 on whichis stored one or more sets of instructions and data structures (e.g.,instructions 924) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 924 mayalso reside, completely or at least partially, within the main memory904 and/or within the processor 902 during execution thereof by thecomputer system 900, the main memory 904 and the processor 902 alsoconstituting machine-readable media.

The instructions 924 may further be transmitted or received over anetwork 926 via the network interface device 920 utilizing any one of anumber of well-known transfer protocols (e.g., HTTP).

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions 924. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding or carrying a set of instructions 924for execution by the machine and that cause the machine to perform anyone or more of the methodologies of the present invention, or that iscapable of storing, encoding or carrying data structures utilized by orassociated with such a set of instructions 924. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment.

What is claimed is:
 1. A computer-implemented method comprising:collecting defective conditions data pertaining to listings of itemsposted on a network-based publication system, the defective conditionsdata identifying at least one of a defective link, a defective image, ora defective format included in the listings; identifying a first portionof a first listing of the listings, the first portion defective based onthe defective conditions; correcting the first portion of the firstlisting; identifying a second portion of the first listing, the secondportion defective based on the defective conditions; detecting that thesecond portion is uncorrected after at least a predetermined amount oftime; and deleting the uncorrected second portion of the first listingafter expiration of the predetermined amount of time.
 2. Thecomputer-implemented method of claim 1, further comprising: identifyinga second listing of the listings, the second listing defective based onthe defective conditions; and correcting the second listing.
 3. Thecomputer-implemented method of claim 1, further comprising: identifyinga second listing of the listings that is defective based on thedefective conditions; detecting that the second listing is uncorrectedfor at least the predetermined amount of time; and deleting theuncorrected second listing after expiration of the predetermined amountof time.
 4. The computer-implemented method of claim 1, furthercomprising: analyzing a quality of the first listing posted on thenetwork-based publication system, the analyzing comprising: determiningwhether a title of the first listing of an item for sale corresponds toa name of the item; and determining whether a description of the item inthe first listing matches the name of the item and the title of thefirst listing.
 5. The computer-implemented method of claim 1, furthercomprising: determining that a title of the first listing of an item forsale does not correspond to a name of the item; correcting the title ofthe first listing based on the name of the item; determining that adescription of the item in the first listing does not match the name ofthe item and the title of the first listing; detecting that thedescription of the item in the first listing is uncorrected for at leastthe predetermined amount of time; and deleting the description of theitem in the first listing after expiration of the predetermined amountof time.
 6. The computer-implemented method of claim 1, furthercomprising: in response to identifying the second portion of the firstlisting, requesting a correction input; in response to requesting thecorrection input, detecting that no user input correcting the secondportion has been received after at least the predetermined amount oftime and detecting that the second portion is uncorrected after at leastthe predetermined amount of time; and deleting the second portion of thefirst listing after expiration of the predetermined amount of time. 7.The computer-implemented method of claim 1, further comprising: inresponse to identifying the second portion of the first listing,requesting a correction input; in response to requesting the correctioninput, receiving a user input within the predetermined amount of time;and correcting the second portion of the first listing based on the userinput.
 8. The computer-implemented method of claim 1, furthercomprising: in response to identifying the second portion of the firstlisting, requesting a correction input; in response to requesting thecorrection input, receiving a user input after the predetermined amountof time; and deleting the second portion of the first listing afterexpiration of the predetermined amount of time.
 9. Thecomputer-implemented method of claim 1, wherein the correcting the firstportion of the first listing includes using the defective conditionsdata and a learning module to identify a correct link, correct image, orcorrect format for repairing the first portion of the listing, whereinthe method further comprises: reporting defective conditions of thelistings of the items posted on the network-based publication systembased on the defective conditions data, wherein the second portion ofthe first listing is determined based on an inability of the learningmodule to automatically correct the second portion of the first listing.10. A system comprising: one or more computer processors; one or morememories; and a set of instructions incorporated into the one or morememories; the set of instructions configuring the one or more computerprocessors to perform operations for automatically correcting listingsof items posted on a network-based publication system, the operationscomprising: collecting defective conditions data pertaining to thelistings, the defective conditions data identifying at least one of adefective link, a defective image, or a defective format included in thelistings; identifying a first portion of a first listing of thelistings, the first portion defective based on the defective conditions;correcting the first portion of the first listing; identifying a secondportion of the first listing, the second portion defective based on thedefective conditions; detecting that the second portion is uncorrectedafter at least a predetermined amount of time; and deleting theuncorrected second portion of the first listing after expiration of thepredetermined amount of time.
 11. The system of claim 10, furthercomprising: identifying a second listing of the listings, the secondlisting defective based on the defective conditions; and correcting thesecond listing.
 12. The system of claim 10, further comprising:identifying a second listing of the listings that is defective based onthe defective conditions; detecting that the second listing isuncorrected for at least the predetermined amount of time; and deletingthe uncorrected second listing after expiration of the predeterminedamount of time.
 13. The system of claim 10, further comprising:analyzing a quality of the first listing posted on the network-basedpublication system, the analyzing comprising: determining whether atitle of the first listing of an item for sale corresponds to a name ofthe item; and determining whether a description of the item in the firstlisting matches the name of the item and the title of the first listing.14. The system of claim 10 further comprising: determining that a titleof the first listing of an item for sale does not correspond to a nameof the item; correcting the title of the first listing based on the nameof the item; determining that a description of the item in the firstlisting does not match the name of the item and the title of the firstlisting; detecting that the description of the item in the first listingis uncorrected for at least the predetermined amount of time; anddeleting the description of the item in the first listing afterexpiration of the predetermined amount of time.
 15. The system of claim10, further comprising: in response to identifying the second portion ofthe first listing, requesting a correction input; in response torequesting the correction input, detecting that no user input correctingthe second portion has been received after at least the predeterminedamount of time and detecting that the second portion is uncorrectedafter at least the predetermined amount of time; and deleting the secondportion of the first listing after expiration of the predeterminedamount of time.
 16. The system of claim 10, further comprising: inresponse to identifying the second portion of the first listing,requesting a correction input; in response to requesting the correctioninput, receiving a user input within the predetermined amount of time;and correcting the second portion of the first listing based on the userinput.
 17. The system of claim 10, further comprising: in response toidentifying the second portion of the first listing, requesting acorrection input; in response to requesting, the correction input,receiving a user input after the predetermined amount of time; anddeleting the second portion of the first listing after expiration of thepredetermined amount of time.
 18. The system of claim 10, wherein thecorrecting the first portion of the first listing includes using thedefective conditions data and a learning module to identify a correctlink, correct image, or correct format for repairing the first portionof the listing.
 19. The system of claim 18, further comprising:reporting defective conditions of the listings of the items posted onthe network-based publication system based on the defective conditionsdata, wherein the second portion of the first listing is determinedbased on an inability of the learning module to automatically correctthe second portion of the first listing.
 20. A non-transitorymachine-readable medium comprising a set of instructions that, whenimplemented by one more computer processors, cause the one or morecomputer processors to perform operations for automatically correctinglistings of items posted on a network-based publication system, theoperations comprising: collecting defective conditions data pertainingto listings of items posted on a network-based publication system, thedefective conditions data identifying at least one of a defective link,a defective image, or a defective format included in the listings;identifying a first portion of a first listing of the listings, thefirst portion defective based on the defective conditions; correctingthe first portion of the first listing; identifying a second portion ofthe first listing, the second portion defective based on the defectiveconditions; detecting that the second portion is uncorrected after atleast a predetermined amount of time; and deleting the uncorrectedsecond portion of the first listing after expiration of thepredetermined amount of time.